Getting Started with Data Science

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Brief History Background:

The Art of uncovering the insights and trends in Data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is Data Science.

Data Science is the hottest field of the century. Data science, artificial intelligence (AI) and machine learning are revolutionising the way people do business and research around the world.

Basic Definition of Who is a Data Scientist: 

By Murtaza Haider, Ph.D., and Author of the Book Getting Started with Data Science: Making Sense of Data with Analytics.

“While the world is awash with large volumes of data, inexpensive computing power, and vast amounts of digital storage, the skilled workforce capable of analysing data and interpreting it is in short supply.

It may surprise you to know that individuals and corporations are battling over who is a data scientist. My definition is rather simple and straightforward. If you analyse data to find solutions to problems and are able to tell a compelling story from your findings, you are a data scientist.”

The Kinds of Data Scientist:

By Yael Garten, Director of Siri Data Science and Engineering at Apple.

“In 2012, HBR dubbed data scientist “the sexiest job of the 21st century”. It is also, arguably, the vaguest. To hire the right people for the right roles, it’s important to distinguish between different types of data scientist. There are plenty of different distinctions that one can draw, of course, and any attempt to group data scientists into different buckets is by necessity an oversimplification. Nonetheless, I find it helpful to distinguish between the deliverables they create. One type of data scientist creates output for humans to consume, in the form of product and strategy recommendations. They are decision scientists. The other creates output for machines to consume like models, training data, and algorithms. They are modeling scientists.

The elusive full stack data scientists do exist, though they are hard to find. In most organizations, it makes sense for data scientists to specialize into one type or another. But data scientists are curious creatures who thrive from being able to creatively dabble; there are benefits to giving them flexibility to work on projects that touch both “types” – both for them and for the organization.”